Heroku connector

Use your Heroku data for reporting, automation and AI.

Data Panda brings your Heroku app, dyno, add-on and release data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your engineering, finance and on-call teams use every day.

About Heroku

The managed app platform behind a lot of Rails, Node and Python production traffic.

Heroku is a managed application platform owned by Salesforce. You push a git branch, the platform builds it with a buildpack, and a dyno runs the resulting container on a shared or private runtime. Around that core sit Heroku Postgres, the Heroku Key-Value Store for Redis, the Elements Marketplace with around 150 add-ons, Heroku Connect for two-way sync with Salesforce objects, and Heroku Flow with pipelines and review apps tied into GitHub.

The platform metadata your apps generate is what becomes interesting in a warehouse. Dyno hours per app and per process type, release frequency and rollback rate per pipeline, add-on spend by tier and by app, Postgres connection counts and slow-query patterns, and the membership and access trail across Heroku Teams. Once that lives next to your billing, your CRM and your incident data, you can answer the rightsizing, renewal and reliability questions that the Heroku dashboard alone can show one app at a time.

What your Heroku data is for

What you get once Heroku is connected.

Platform and spend reporting

Dyno hours, add-on cost, release cadence and Postgres health in one view, across pipelines and apps.

Dyno hours by app, process type and tier, against the monthly invoice line
Add-on spend per app, broken out by Postgres, Redis and third-party add-ons from the Elements marketplace
Release frequency and rollback rate per pipeline, with the team and pipeline owner attached

Process automation

Turn release, dyno and add-on events into the right work in the systems your engineering and finance teams already use.

Open a Linear or Jira ticket when a pipeline rollback fires twice in a week on the same app
Page the on-call channel when a Postgres database crosses 80 percent of its plan limit on rows or storage
Flag dynos sitting under 15 percent CPU for a full week as rightsizing candidates for the next renewal review

AI workflows

Put release, dyno and incident history behind AI that knows how your apps behave in production.

Anomaly scoring on dyno memory and response time per release tag, against a rolling baseline
Release-note drafts from the commits and PRs between two production deploys on a pipeline
Cost-anomaly assistant that flags add-on plan jumps and explains which app drove the change

Custom apps on your data

Internal tools on Heroku metadata that engineering and platform leads keep rebuilding as one-off scripts.

Dyno-rightsizing workbench with CPU, memory and request volume per app, per tier
Release-versus-incident view that joins pipeline deploys to the on-call incident timeline
Add-on renewal console with plan, usage and contract date per app, ranked by next renewal

Use cases

Use cases we deliver with Heroku data.

A list of concrete reports, automations and AI features we have built on Heroku data. Pick the one that matches your situation.

Dyno hours per appHours by app, process type and tier, against the dyno line on the monthly invoice.

Dyno rightsizingCPU, memory and request volume per dyno over a rolling window, with tier-down candidates flagged.

Release frequencyProduction releases per pipeline per week, broken out by team and app.

Rollback rateReleases followed by a rollback on the same pipeline within N hours.

Release vs incidentProduction deploys joined to the on-call incident timeline, per app and per pipeline.

Add-on spendMarketplace add-on cost per app and per tier, ranked by month-on-month change.

Postgres tier checkPostgres plan limits, used storage, row count and cache hit rate per database.

Postgres slow queriesTop slow-query patterns per database, with mean and p95 duration.

Build durationBuildpack and slug-build time per app and per pipeline, trending over time.

Review-app sprawlOpen review apps per pipeline, with creator, age and last-deploy time.

Heroku Teams accessMembers per team and per app, with role and last-active date.

Heroku Connect healthMappings, sync errors and lag per Salesforce object on Heroku Connect.

Real business questions

Answers you will finally get.

Which apps are oversized for what they run?

CPU, memory and request volume per dyno over the last 30 to 90 days, against the tier the app is provisioned on. An app sitting on a Standard-2X that never crosses 15 percent CPU shows up as a tier-down candidate alongside the monthly cost saving, instead of staying as a checkbox on someone's quarterly cleanup list.

Are our releases causing the incidents we are paging on?

Pipeline release timestamps joined to the on-call incident timeline per app. The pattern of incidents that follow a deploy within an hour, versus incidents that look unrelated to release activity, becomes a number per pipeline rather than a hunch the platform team carries between retros.

Where is add-on spend drifting?

Marketplace add-on cost per app and per tier, with month-on-month change and the date the plan was bumped up. Finance sees which Heroku Postgres or Redis plans crept up over the year, and engineering sees the apps where the next renewal is the moment to revisit the tier.

Value for everyone in the organisation

Where each function gets value.

For finance leaders

Heroku spend per app, broken out across dyno hours, Heroku Postgres, Heroku Key-Value Store and third-party add-ons. Renewal and tier-true-up conversations start with usage data per app, instead of a single Heroku line in the SaaS-spend deck.

For sales leaders

Customer-facing app uptime, response time and incident windows joined back to the CRM account. Account managers see whether the app a strategic customer leans on had a rough week, before the renewal call rather than during it.

For operations

Release frequency, rollback rate, dyno health and Postgres tier headroom in one view. Engineering leads, platform and SRE share the same numbers per app instead of three exports built the morning of the steerco.

Ideas

What you can automate with Heroku.

Pair with Salesforce

Wire Heroku app health into Salesforce account records

Heroku Connect already syncs Salesforce objects into Heroku Postgres for the apps that need it. We push the other direction too: app uptime, response time and incident windows per customer-facing app land on the linked Salesforce account, with the dyno tier and pipeline behind it. Account executives see the production reality the customer is living in, instead of asking the platform team for screenshots the day before the renewal call.

Pair with GitHub

Tie Heroku releases back to the GitHub PRs that shipped them

Each Heroku pipeline release is matched to the commits and pull requests merged on the connected GitHub repo since the previous deploy tag. Engineering leads get release-scope summaries per pipeline without scrolling Slack history, and incident postmortems start with the actual diff that went out, instead of three people guessing which PR was in the bad release.

Pair with Slack

Route Heroku platform events to the right Slack channel

Pipeline rollbacks, dyno crashes, Postgres plan-limit warnings and Heroku Connect sync errors post into the team or on-call channel with app, pipeline and severity attached. Engineering leads spot the broken release and the database creeping toward its row limit on the channel the team already watches, instead of waiting for the customer ticket to land.

Pair with Snowflake

Land Heroku platform metrics in Snowflake next to product data

Dyno hours, release events, add-on spend and Heroku Postgres usage land as tables in Snowflake alongside the product, billing and CRM data already there. Finance and platform teams join Heroku cost to product usage and customer revenue per app, so the rightsizing and renewal questions get answered against the same warehouse that the rest of the business already reports on.

Your existing tools

Your data lands in a warehouse. Your BI tools read from it.

You keep the reporting tool you already have. We connect it to the warehouse where your Heroku data lives.

Power BI Microsoft

Fabric Microsoft

Snowflake Data warehouse

BigQuery Google

Tableau Visualisation

Excel Sheets & pivots

Three steps

From Heroku to answers in three steps.

Connect securely

OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.

Land in your warehouse

Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.

Reporting, automation, AI

We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.

Two ways to work with us

Pick the track that fits how you work.

Track 01

Self-serve

We set up the foundation. Your team builds on top.

Heroku connector configured and running
Warehouse set up in your cloud account
Clean access for your Power BI, Fabric or Tableau team
Documentation on what's in the data model
Sync monitoring so you're warned before reports break

Best fit Teams that already have a BI analyst or data engineer and want to own the build.

Track 02

Done for you

We build the whole thing, end to end.

Everything in Self-serve
Dashboards built to the questions your team actually asks
Automations between your systems
AI workflows scoped to real tasks your team runs
Custom apps where a dashboard does not cut it
Ongoing delivery at a pace that fits your team

Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.

Before you book

Frequently asked questions.

Who owns the data?

You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.

How fresh is the data?

Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.

Do I need a warehouse already?

No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.

Can the connector pull from Heroku Postgres directly, or only from the platform API?

Both, and most teams want both. The platform API gives you the metadata layer: apps, pipelines, releases, dyno usage, add-on plans, Heroku Teams membership and Heroku Connect mappings. A direct Heroku Postgres pull on top of that gives you the application-data layer your apps write to. Together they answer questions the platform API alone cannot, like joining a release tag to the rows the app produced in the hour after the deploy.

Does the connector work with Private Spaces and Heroku Shield?

Yes. Private Spaces and Shield apps expose the same platform API surface for metadata and add-on inventory, so the dyno usage, release and pipeline reporting works the same way as on common runtime apps. For Postgres pulls inside a Private Space, the network path is configured per environment so the warehouse pull respects the same trusted-IP and audit rules the rest of your Private Space already enforces.

GDPR-compliant

Data stays in the EU

You own the warehouse

A first deliverable live in four to six weeks.

We review your Heroku setup and the systems around it. Together we pick the first thing worth building.

Book a call See our other connectors